Detecting the Information of Functional Connectivity Networks in Normal Aging Using Deep Learning From a Big Data Perspective.

ABSTRACT: A resting-state functional connectivity (rsFC)-constructed functional network (FN) derived from functional magnetic resonance imaging (fMRI) data can effectively mine alterations in brain function during aging due to the non-invasive and effective advantages of fMRI. With global health research focusing on aging, several open fMRI datasets have been made available that combine deep learning with big data and are a new, promising trend and open issue for brain information detection in fMRI studies of brain aging. In this study, we proposed a new method based on deep learning from the perspective of big data, named Deep neural network (DNN) with Autoencoder (AE) pretrained Functional connectivity Analysis (DAFA), to deeply mine the important functional connectivity changes in fMRI during bra

SUBMITTER: Wen X 

PROVIDER: S-EPMC6978665 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature